From af713470c12b05395f52b4b47c02f03ec6af7c67 Mon Sep 17 00:00:00 2001
From: pixelpaws <willmiao.dev@gmail.com>
Date: Thu, 25 Sep 2025 15:59:44 +0800
Subject: [PATCH] fix(recipes): parse loras from civitai hashes

---
 py/recipes/parsers/civitai_image.py         | 54 +++++++++++++++++-
 tests/services/test_civitai_image_parser.py | 61 +++++++++++++++++++++
 2 files changed, 114 insertions(+), 1 deletion(-)
 create mode 100644 tests/services/test_civitai_image_parser.py

diff --git a/py/recipes/parsers/civitai_image.py b/py/recipes/parsers/civitai_image.py
index 31234ab9..409c5fa3 100644
--- a/py/recipes/parsers/civitai_image.py
+++ b/py/recipes/parsers/civitai_image.py
@@ -284,7 +284,59 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                             logger.error(f"Error fetching Civitai info for model ID {version_id}: {e}")
                     
                         result["loras"].append(lora_entry)
-            
+
+            # If we found LoRA hashes in the metadata but haven't already
+            # populated entries for them, fall back to creating LoRAs from
+            # the hashes section. Some Civitai image responses only include
+            # LoRA information here without explicit resources entries.
+            for lora_name, lora_hash in lora_hashes.items():
+                if not lora_hash:
+                    continue
+
+                # Skip LoRAs we've already added via resources or other fields
+                if lora_hash in added_loras:
+                    continue
+
+                lora_entry = {
+                    'name': lora_name,
+                    'type': "lora",
+                    'weight': 1.0,
+                    'hash': lora_hash,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': lora_name,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+
+                if metadata_provider:
+                    try:
+                        civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
+
+                        populated_entry = await self.populate_lora_from_civitai(
+                            lora_entry,
+                            civitai_info,
+                            recipe_scanner,
+                            base_model_counts,
+                            lora_hash
+                        )
+
+                        if populated_entry is None:
+                            continue
+
+                        lora_entry = populated_entry
+
+                        if 'id' in lora_entry and lora_entry['id']:
+                            added_loras[str(lora_entry['id'])] = len(result["loras"])
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA hash {lora_hash}: {e}")
+
+                added_loras[lora_hash] = len(result["loras"])
+                result["loras"].append(lora_entry)
+
             # Check for LoRA info in the format "Lora_0 Model hash", "Lora_0 Model name", etc.
             lora_index = 0
             while f"Lora_{lora_index} Model hash" in metadata and f"Lora_{lora_index} Model name" in metadata:
diff --git a/tests/services/test_civitai_image_parser.py b/tests/services/test_civitai_image_parser.py
new file mode 100644
index 00000000..54353336
--- /dev/null
+++ b/tests/services/test_civitai_image_parser.py
@@ -0,0 +1,61 @@
+import pytest
+
+from py.recipes.parsers.civitai_image import CivitaiApiMetadataParser
+
+
+@pytest.mark.asyncio
+async def test_parse_metadata_creates_loras_from_hashes(monkeypatch):
+    async def fake_metadata_provider():
+        return None
+
+    monkeypatch.setattr(
+        "py.recipes.parsers.civitai_image.get_default_metadata_provider",
+        fake_metadata_provider,
+    )
+
+    parser = CivitaiApiMetadataParser()
+
+    metadata = {
+        "Size": "1536x2688",
+        "seed": 3766932689,
+        "Model": "indexed_v1",
+        "steps": 30,
+        "hashes": {
+            "model": "692186a14a",
+            "LORA:Jedst1": "fb4063c470",
+            "LORA:HassaKu_style": "3ce00b926b",
+            "LORA:DetailedEyes_V3": "2c1c3f889f",
+            "LORA:jiaocha_illustriousXL": "35d3e6f8b0",
+            "LORA:绪儿 厚涂构图光影质感增强V3": "d9b5900a59",
+        },
+        "prompt": "test",
+        "Version": "ComfyUI",
+        "sampler": "er_sde_ays_30",
+        "cfgScale": 5,
+        "clipSkip": 2,
+        "resources": [
+            {
+                "hash": "692186a14a",
+                "name": "indexed_v1",
+                "type": "model",
+            }
+        ],
+        "Model hash": "692186a14a",
+        "negativePrompt": "bad",
+        "username": "LumaRift",
+        "baseModel": "Illustrious",
+    }
+
+    result = await parser.parse_metadata(metadata)
+
+    assert result["base_model"] == "Illustrious"
+    assert len(result["loras"]) == 5
+    assert all(lora["weight"] == 1.0 for lora in result["loras"])
+    assert {lora["name"] for lora in result["loras"]} == {
+        "Jedst1",
+        "HassaKu_style",
+        "DetailedEyes_V3",
+        "jiaocha_illustriousXL",
+        "绪儿 厚涂构图光影质感增强V3",
+    }
+